Based on our record, Scikit-learn should be more popular than UserVoice. It has been mentiond 29 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
I have sent them some user stories, complete with storyboard images. I am not a beta participant though. I suggested that it would be helpful if they rolled out something like UserVoice's Feedback Manager, so there is transparency to what's in the request queue and where we users can formally vote on the value of a given feature suggestion. Source: over 1 year ago
- Collecting customer's feature requests: This is a tough one, I am using https://uservoice.com/ but I don't like it that much. I am searching for a self-hosted alternative to https://canny.io/. - Source: Hacker News / over 2 years ago
I think that RM should consider a solution such as UserVoice which will let the user community vote on what critical bug fixes or new features we feel are most important. Not only can a user upvote a feature request or critical fix, but they can also add comments to help substantiate their vote. Source: over 2 years ago
Six months later, UserVoice wanted to sign in with Courier and mentioned we lacked some functionalities they wanted. Specifically, they wanted to put two blocks next to each other in the notification designer. So, the sales and product team reached out to me, and I went, “Oh yeah, I hacked that together; it was cool but with a few bugs.”. - Source: dev.to / over 2 years ago
Would having a proper Customer Feedback platform using something like Canny or UserVoice be useful? Source: almost 3 years ago
How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 22 days ago
Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / 4 months ago
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / about 1 year ago
The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: about 1 year ago
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: about 1 year ago
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